Last updated: 2025-09-19

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Knit directory: PIPAC_spatial/

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Ignored files:
    Ignored:    .RData
    Ignored:    analysis/figure/
    Ignored:    cell_annotation_top_markers_denoist.tsv
    Ignored:    cell_main_cluster_marker_annotations_denoist.tsv
    Ignored:    celltype_markers.tsv
    Ignored:    code/.Rhistory
    Ignored:    code/RSC_latest_EDM_2025-08-06/
    Ignored:    immune_cluster_marker_annotations.tsv
    Ignored:    immune_cluster_marker_annotations_2ndpass.tsv
    Ignored:    main_cluster_marker_annotations.tsv
    Ignored:    nonimmune_cluster_marker_annotations.tsv
    Ignored:    nonimmune_cluster_marker_annotations_2ndpass.tsv

Untracked files:
    Untracked:  Rplots.pdf
    Untracked:  analysis/.ipynb_checkpoints/
    Untracked:  analysis/Xenium_preprocess_wholecell.Rmd
    Untracked:  analysis/annotation_cell.Rmd
    Untracked:  analysis/clustering.ipynb
    Untracked:  analysis/compare_cellular_nuclear.Rmd
    Untracked:  analysis/compare_cellular_nuclear_denoist_annot.Rmd
    Untracked:  analysis/denoising_cellular_transcripts.Rmd
    Untracked:  analysis/metrics.Rmd
    Untracked:  analysis/niche_construction_ncells3k_nneighbors20.Rmd
    Untracked:  analysis/niche_silhouette.ipynb
    Untracked:  analysis/plot_PRGS.Rmd
    Untracked:  analysis/proximity.Rmd
    Untracked:  analysis/seurat_to_anndata.ipynb
    Untracked:  code/Proximity_analysis/
    Untracked:  code/construct_niches_tumor.R
    Untracked:  code/construct_niches_tumor.Rout
    Untracked:  code/denoist.R
    Untracked:  code/denoist.Rout
    Untracked:  code/parse_denoist_res.R
    Untracked:  code/seurat_to_anndata.R
    Untracked:  code/slurm.24884614.err
    Untracked:  code/slurm.24884614.out
    Untracked:  code/slurm.25014505.err
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    Untracked:  code/slurm.25630169.err
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    Untracked:  niche_composition_k5_n30.pdf
    Untracked:  timepoint_celltypeprop_barplot_arm2.pdf
    Untracked:  timepoint_celltypeprop_barplot_arm3.pdf
    Untracked:  timepoint_nicheprop_barplot_arm2_arm3.pdf

Unstaged changes:
    Modified:   .gitignore
    Modified:   analysis/Xenium_processing.Rmd
    Modified:   analysis/add_metadata.Rmd
    Modified:   analysis/annotation.Rmd
    Modified:   analysis/arm3_DEGs.Rmd
    Modified:   analysis/arm3_comparative_analysis.Rmd
    Modified:   analysis/arm3_niche_comparison.Rmd
    Modified:   analysis/feature_expression.Rmd
    Modified:   analysis/niche_construction.Rmd
    Modified:   analysis/pca_variance_decomp.Rmd
    Modified:   analysis/plot_by_group.Rmd
    Modified:   analysis/post_clustering.Rmd
    Modified:   analysis/splitting_samples.Rmd
    Modified:   annotation_dimplot.pdf
    Modified:   code/PIPAC_colors_themes.R
    Modified:   code/anndata_to_seurat.R
    Modified:   code/construct_niches.R
    Modified:   code/plot_metadata.R
    Modified:   code/plot_save_pdf.R
    Modified:   code/rapids_pipeline/.ipynb_checkpoints/clustering-checkpoint.ipynb
    Modified:   code/rapids_pipeline/.ipynb_checkpoints/seurat_to_anndata-checkpoint.ipynb
    Modified:   code/rapids_pipeline/clustering.ipynb
    Modified:   code/rapids_pipeline/seurat_to_anndata.ipynb
    Modified:   code/run_rscript.sh
    Modified:   code/update_metadata.R
    Modified:   demographics_grid.pdf
    Modified:   output/scrna-Meanplot.pdf
    Modified:   output/scrna-Variance.csv
    Modified:   output/scrna-VarianceExplained-Boxplot.pdf

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Load packages

suppressPackageStartupMessages({
  library(workflowr)
  library(arrow)
  library(Seurat)
  library(SeuratObject)
  library(SeuratDisk)
  library(tidyverse)
  library(tibble)
  library(ggplot2)
  library(ggpubr)
  library(ggrepel)
  library(googlesheets4)
  library(workflowr)
  library(patchwork)
  library(scProportionTest)})

Environment variables and helper functions

setwd("/home/hnatri/PIPAC_spatial/")
set.seed(9999)
options(future.globals.maxSize = 30000 * 1024^2)
options(scipen = 99999)
options(ggrepel.max.overlaps = Inf)

source("/home/hnatri/PIPAC_spatial/code/PIPAC_colors_themes.R")
source("/home/hnatri/PIPAC_spatial/code/plot_functions.R")

# Lineage info
gs4_deauth()
metadata  <- gs4_get("https://docs.google.com/spreadsheets/d/1sXXwOreLxjMSUoPt79c6jmaQpluWkaxA5P5HfDsed3I/edit?usp=sharing")
lineage <- read_sheet(metadata, sheet = "Cell type annotations")

# Calling DEGs between two groups for each cell type
get_DEGs <- function(seuratdata, ctvar, celltypes, groupvar, group1, group2){
  #message(xx)
  DEGlist <- lapply(celltypes, function(xx){
    seuratdata$DEGgroup <- seuratdata@meta.data[,ctvar]
    data_subset <- subset(seuratdata, subset = DEGgroup == xx)
    Idents(data_subset) <- as.character(unlist(data_subset[[groupvar]]))
    
    if (min(table(unlist(data_subset[[groupvar]])))<20){
      return(NULL)
    }
    
    if (all((c(group1, group2) %in% unlist(data_subset[[groupvar]]) == c(T, T)))){
      markers <- FindMarkers(data_subset,
                             ident.1 = group1,
                             ident.2 = group2,
                             assay = "RNA",
                             verbose = F)
      markers$feature <- rownames(markers)
      markers[,ctvar] <- xx
      
      return(markers)
    } else {
      return(NULL)
    }
    })
  
  as.data.frame(do.call(rbind, DEGlist))
}

Import data

seurat_data <- readRDS("/tgen_labs/banovich/PIPAC/Seurat/cell_merged_spatial_filtered_splitsamples_clustered_NC50_NN20_PC20_Seurat_denoIST_metadata_ncells3k_nk20_niches.rds")

Niche construction

See /code/construct_niches.R

Plotting niche composition

plot_list <- lapply(seq(3, 8), function(niche){
  niche_column <- paste0("ncells3k_niche_k", niche, "_n20")
  niches <- seq(1, niche)
  seurat_data@meta.data[,niche_column] <- factor(seurat_data@meta.data[,niche_column],
                                                 levels = niches)
  
  create_barplot(seurat_data,
                 group_var = niche_column,
                 plot_var = "Annotation",
                 group_levels = niches,
                 plot_levels = sort(unique(seurat_data$Annotation)),
                 plot_colors = pipac_celltype_col,
                 var_names =  c("Frequency (%)", ""),
                 legend_title = "Cell type")
  
})

plot_list
[[1]]


[[2]]


[[3]]


[[4]]


[[5]]


[[6]]

Plotting by tissue

plot_list <- lapply(seq(3, 8), function(niche){
  niche_column <- paste0("ncells3k_niche_k", niche, "_n20")
  ct_table <- as.data.frame(table(seurat_data@meta.data[, niche_column], seurat_data$Tissue))
  colnames(ct_table) <- c("Niche", "Tissue", "Freq")
  ct_table <- spread(ct_table, Niche, Freq)
  # Converting to percetange
  #ct_table[,2:length(ct_table)] <- (ct_table[,2:length(ct_table)]/rowSums(ct_table[,2:length(ct_table)]))*100
  ct_table <- gather(ct_table, Niche, Freq, names(ct_table)[2:length(names(ct_table))], factor_key=TRUE)
      
  ct_table$Tissue <- factor(ct_table$Tissue, levels = c("Normal", "Tumor"))
  
  niches <- seq(1, niche)
  niches <- factor(niches, levels = niches)
  #niches <- sort(unique(seurat_data@meta.data[, niche_col]))
  niche_col <- colorRampPalette(brewer.pal(10, "Paired"))(nb.cols <- length(niches))
  names(niche_col) <- levels(niches)
  
  ggplot(ct_table, aes(x = Tissue, y = Freq, fill = Niche)) +
         geom_bar(stat="identity", position='stack', width = 0.8) +
         scale_fill_manual(name = "Niche", values = niche_col) +
         xlab("") +
         ylab("Cell count") +
         theme_classic() +
         theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
  
})

plot_list
[[1]]


[[2]]


[[3]]


[[4]]


[[5]]


[[6]]

Plotting by sample

plot_list <- lapply(seq(3, 8), function(niche){
  niche_column <- paste0("ncells3k_niche_k", niche, "_n20")
  niches <- seq(1, niche)
  seurat_data@meta.data[,niche_column] <- factor(seurat_data@meta.data[,niche_column],
                                                 levels = niches)
  niche_col <- colorRampPalette(brewer.pal(10, "Paired"))(nb.cols <- length(niches))
  names(niche_col) <- levels(niches)
  
  create_barplot(seurat_data,
                 plot_var = niche_column,
                 group_var = "Sample",
                 plot_levels = niches,
                 group_levels = sort(unique(seurat_data$Sample)),
                 plot_colors = niche_col,
                 var_names =  c("Frequency (%)", ""),
                 legend_title = "Niche")
  
})

plot_list

Plotting by sample, Arm 3 tumors only

arm3_tumor <- subset(seurat_data, subset = Arm == "Arm3" &
                       Tissue == "Tumor")

plot_list <- lapply(seq(3, 8), function(niche){
  niche_column <- paste0("ncells3k_niche_k", niche, "_n20")
  niches <- seq(1, niche)
  arm3_tumor@meta.data[,niche_column] <- factor(arm3_tumor@meta.data[,niche_column],
                                                levels = niches)
  niche_col <- colorRampPalette(brewer.pal(10, "Paired"))(nb.cols <- length(niches))
  names(niche_col) <- levels(niches)
  
  create_barplot(arm3_tumor,
                 plot_var = niche_column,
                 group_var = "Sample",
                 plot_levels = niches,
                 group_levels = sort(unique(arm3_tumor$Sample)),
                 plot_colors = niche_col,
                 var_names =  c("Frequency (%)", ""),
                 legend_title = "Niche")
  
})

plot_list
[[1]]


[[2]]


[[3]]


[[4]]


[[5]]


[[6]]

Plotting by sample, Arm 3 tumor adjacent only

arm3_normal <- subset(seurat_data, subset = Arm == "Arm3" &
                        Tissue == "Normal")

plot_list <- lapply(seq(3, 8), function(niche){
  niche_column <- paste0("ncells3k_niche_k", niche, "_n20")
  niches <- seq(1, niche)
  arm3_normal@meta.data[,niche_column] <- factor(arm3_normal@meta.data[,niche_column],
                                                 levels = niches)
  niche_col <- colorRampPalette(brewer.pal(10, "Paired"))(nb.cols <- length(niches))
  names(niche_col) <- levels(niches)
  
  create_barplot(arm3_normal,
                 plot_var = niche_column,
                 group_var = "Sample",
                 plot_levels = niches,
                 group_levels = sort(unique(arm3_normal$Sample)),
                 plot_colors = niche_col,
                 var_names =  c("Frequency (%)", ""),
                 legend_title = "Niche")
  
})

plot_list
[[1]]


[[2]]


[[3]]


[[4]]


[[5]]


[[6]]

Spatial plots of niches

plot_list <- lapply(seq(3, 8), function(niche){
  
  niche_column <- paste0("ncells3k_niche_k", niche, "_n20")
  niches <- seq(1, niche)
  niches <- factor(niches, levels = niches)
  #niches <- sort(unique(seurat_data@meta.data[, niche_col]))
  niche_col <- colorRampPalette(brewer.pal(10, "Paired"))(nb.cols <- length(niches))
  names(niche_col) <- levels(niches)
  
  DimPlot(seurat_data,
          group.by = niche_column,
          cols = niche_col,
          reduction = "sp_adj") +
  coord_fixed()
  
})

plot_list
[[1]]


[[2]]


[[3]]


[[4]]


[[5]]


[[6]]

Dotplot of cell assignment to niches by celltype

niche_ct_prop <- table(seurat_data$Annotation, seurat_data$ncells3k_niche_k5_n20)
niche_ct_prop <- prop.table(niche_ct_prop, margin = 2)
niche_ct_prop <- niche_ct_prop %>%
  as.data.frame()

# Adding lineage
niche_ct_prop$lineage <- mapvalues(niche_ct_prop$Var1,
                                   from = lineage$Annotation,
                                   to = lineage$Lineage)

niche_ct_prop$Var1 <- factor(niche_ct_prop$Var1,
                             levels = rev(unique(niche_ct_prop$Var1)))

p1 <- ggplot(niche_ct_prop, aes(x = Var2, y = Var1)) +
  geom_point(aes(size = Freq, 
                 #color = value,
                 fill = Var1), 
             stroke = 0.5,
             shape = 21) +
  guides(color = FALSE) +
  scale_fill_manual(values = pipac_celltype_col) +
  theme_bw() +
  xlab("") +
  ylab("") +
  facet_grid(lineage ~ ., scales = "free", space = "free")

# k6
niche_ct_prop <- table(seurat_data$Annotation, seurat_data$ncells3k_niche_k6_n20)
niche_ct_prop <- prop.table(niche_ct_prop, margin = 2)
niche_ct_prop <- niche_ct_prop %>%
  as.data.frame()

niche_ct_prop$lineage <- mapvalues(niche_ct_prop$Var1,
                                   from = lineage$Annotation,
                                   to = lineage$Lineage)

niche_ct_prop$Var1 <- factor(niche_ct_prop$Var1,
                             levels = rev(unique(niche_ct_prop$Var1)))

p2 <- ggplot(niche_ct_prop, aes(x = Var2, y = Var1)) +
  geom_point(aes(size = Freq, 
                 #color = value,
                 fill = Var1), 
             stroke = 0.5,
             shape = 21) +
  guides(color = FALSE) +
  scale_fill_manual(values = pipac_celltype_col) +
  theme_bw() +
  xlab("") +
  ylab("") +
  facet_grid(lineage ~ ., scales = "free", space = "free")

# k7
niche_ct_prop <- table(seurat_data$Annotation, seurat_data$ncells3k_niche_k7_n20)
niche_ct_prop <- prop.table(niche_ct_prop, margin = 2)
niche_ct_prop <- niche_ct_prop %>%
  as.data.frame()

niche_ct_prop$lineage <- mapvalues(niche_ct_prop$Var1,
                                   from = lineage$Annotation,
                                   to = lineage$Lineage)

niche_ct_prop$Var1 <- factor(niche_ct_prop$Var1,
                             levels = rev(unique(niche_ct_prop$Var1)))

p3 <- ggplot(niche_ct_prop, aes(x = Var2, y = Var1)) +
  geom_point(aes(size = Freq, 
                 #color = value,
                 fill = Var1), 
             stroke = 0.5,
             shape = 21) +
  guides(color = FALSE) +
  scale_fill_manual(values = pipac_celltype_col) +
  theme_bw() +
  xlab("") +
  ylab("") +
  facet_grid(lineage ~ ., scales = "free", space = "free")

p1

p2

p3

Niche proportions by timepoint in Arm 3

arm3 <- subset(seurat_data, subset = Arm == "Arm3")

niches <- factor(seq(1, 5), levels = seq(1, 5))
niche_col <- colorRampPalette(brewer.pal(10, "Paired"))(nb.cols <- length(niches))
names(niche_col) <- as.character(niches)

create_barplot(arm3,
               plot_var = "ncells3k_niche_k5_n20",
               group_var = "Timepoint",
               plot_levels = niches,
               group_levels = c("0", "6"),
               plot_colors = niche_col,
               var_names =  c("Frequency (%)", ""),
               legend_title = "Niche") +
  ggtitle("Arm 3, all samples")

create_barplot(arm3_tumor,
               plot_var = "ncells3k_niche_k5_n20",
               group_var = "Timepoint",
               plot_levels = niches,
               group_levels = c("0", "6"),
               plot_colors = niche_col,
               var_names =  c("Frequency (%)", ""),
               legend_title = "Niche") +
  ggtitle("Arm 3, tumor")

create_barplot(arm3_normal,
               plot_var = "ncells3k_niche_k5_n20",
               group_var = "Timepoint",
               plot_levels = niches,
               group_levels = c("0", "6"),
               plot_colors = niche_col,
               var_names =  c("Frequency (%)", ""),
               legend_title = "Niche") +
  ggtitle("Arm 3, tumor-adjacent")

Testing for significance with scProportionTest

prop_test <- sc_utils(arm3)
prop_test <- permutation_test(
    prop_test, cluster_identity = "ncells3k_niche_k5_n20",
    sample_1 = "0", sample_2 = "6",
    sample_identity = "Timepoint")

permutation_plot(prop_test) +
  ggtitle("All Arm 3 samples")

prop_test <- sc_utils(arm3_tumor)
prop_test <- permutation_test(
    prop_test, cluster_identity = "ncells3k_niche_k5_n20",
    sample_1 = "0", sample_2 = "6",
    sample_identity = "Timepoint")

permutation_plot(prop_test) +
  ggtitle("Arm 3 tumors")

prop_test <- sc_utils(arm3_normal)
prop_test <- permutation_test(
    prop_test, cluster_identity = "ncells3k_niche_k5_n20",
    sample_1 = "0", sample_2 = "6",
    sample_identity = "Timepoint")

permutation_plot(prop_test) +
  ggtitle("Arm 3 tumor-adjacent")

Niche proportions by timepoint in Arm 2

arm2 <- subset(seurat_data, subset = Arm == "Arm2")

arm2$pre_post <- ifelse(arm2$Timepoint == "0", "pre", "post")

arm2_tumor <- subset(arm2, subset = Tissue == "Tumor")

arm2_normal <- subset(arm2, subset = Tissue == "Normal")

niches <- factor(seq(1, 5), levels = seq(1, 5))
niche_col <- colorRampPalette(brewer.pal(10, "Paired"))(nb.cols <- length(niches))
names(niche_col) <- as.character(niches)

create_barplot(arm2,
               plot_var = "ncells3k_niche_k5_n20",
               group_var = "pre_post",
               plot_levels = niches,
               group_levels = c("pre", "post"),
               plot_colors = niche_col,
               var_names =  c("Frequency (%)", ""),
               legend_title = "Niche") +
  ggtitle("Arm 2, all samples")

create_barplot(arm2_tumor,
               plot_var = "ncells3k_niche_k5_n20",
               group_var = "pre_post",
               plot_levels = niches,
               group_levels = c("pre", "post"),
               plot_colors = niche_col,
               var_names =  c("Frequency (%)", ""),
               legend_title = "Niche") +
  ggtitle("Arm 2, tumor")

table(arm2_normal$pre_post, arm2_normal$ncells3k_niche_k5_n20)
      
           1     2     3     4     5
  post   557  5163 21075     0 12551
  pre    665  5496 19484    23 12702
create_barplot(arm2_normal,
               plot_var = "ncells3k_niche_k5_n20",
               group_var = "pre_post",
               plot_levels = niches,
               group_levels = c("pre", "post"),
               plot_colors = niche_col,
               var_names =  c("Frequency (%)", ""),
               legend_title = "Niche") +
  ggtitle("Arm 2, tumor-adjacent")

prop_test <- sc_utils(arm2)
prop_test <- permutation_test(
    prop_test, cluster_identity = "ncells3k_niche_k5_n20",
    sample_1 = "pre", sample_2 = "post",
    sample_identity = "pre_post")

permutation_plot(prop_test) +
  ggtitle("All Arm 2 samples")

prop_test <- sc_utils(arm2_tumor)
prop_test <- permutation_test(
    prop_test, cluster_identity = "ncells3k_niche_k5_n20",
    sample_1 = "pre", sample_2 = "post",
    sample_identity = "pre_post")

permutation_plot(prop_test) +
  ggtitle("Arm 2 tumors")

prop_test <- sc_utils(arm2_normal)
prop_test <- permutation_test(
    prop_test, cluster_identity = "ncells3k_niche_k5_n20",
    sample_1 = "pre", sample_2 = "post",
    sample_identity = "pre_post")

permutation_plot(prop_test) +
  ggtitle("Arm 2 tumor-adjacent")

# To build on command line, run Rscript -e "rmarkdown::render('niche_construction_ncells3k_nneighbors20.Rmd')"
# Then "mv *.html /home/hnatri/PIPAC_spatial/docs/"

sessionInfo()
R version 4.3.0 (2023-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] ComplexHeatmap_2.18.0       viridis_0.6.3              
 [3] viridisLite_0.4.2           circlize_0.4.15            
 [5] plyr_1.8.8                  RColorBrewer_1.1-3         
 [7] scProportionTest_0.0.0.9000 patchwork_1.1.2            
 [9] googlesheets4_1.1.0         ggrepel_0.9.3              
[11] ggpubr_0.6.0                lubridate_1.9.2            
[13] forcats_1.0.0               stringr_1.5.0              
[15] dplyr_1.1.2                 purrr_1.0.1                
[17] readr_2.1.4                 tidyr_1.3.0                
[19] tibble_3.2.1                ggplot2_3.4.2              
[21] tidyverse_2.0.0             SeuratDisk_0.0.0.9021      
[23] Seurat_5.0.1                SeuratObject_5.0.1         
[25] sp_1.6-1                    arrow_12.0.0               
[27] workflowr_1.7.1            

loaded via a namespace (and not attached):
  [1] RcppAnnoy_0.0.20       splines_4.3.0          later_1.3.1           
  [4] cellranger_1.1.0       polyclip_1.10-4        fastDummies_1.7.3     
  [7] lifecycle_1.0.3        rstatix_0.7.2          doParallel_1.0.17     
 [10] rprojroot_2.0.3        globals_0.16.2         processx_3.8.1        
 [13] lattice_0.21-8         hdf5r_1.3.8            MASS_7.3-60           
 [16] backports_1.4.1        magrittr_2.0.3         plotly_4.10.2         
 [19] sass_0.4.6             rmarkdown_2.22         jquerylib_0.1.4       
 [22] yaml_2.3.7             httpuv_1.6.11          sctransform_0.4.1     
 [25] spam_2.9-1             spatstat.sparse_3.0-1  reticulate_1.29       
 [28] cowplot_1.1.1          pbapply_1.7-0          abind_1.4-5           
 [31] Rtsne_0.16             BiocGenerics_0.48.1    git2r_0.32.0          
 [34] S4Vectors_0.40.2       IRanges_2.36.0         irlba_2.3.5.1         
 [37] listenv_0.9.0          spatstat.utils_3.0-3   goftest_1.2-3         
 [40] RSpectra_0.16-1        spatstat.random_3.1-5  fitdistrplus_1.1-11   
 [43] parallelly_1.36.0      leiden_0.4.3           codetools_0.2-19      
 [46] tidyselect_1.2.0       shape_1.4.6            farver_2.1.1          
 [49] stats4_4.3.0           matrixStats_1.0.0      spatstat.explore_3.2-1
 [52] googledrive_2.1.0      jsonlite_1.8.5         GetoptLong_1.0.5      
 [55] ellipsis_0.3.2         progressr_0.13.0       iterators_1.0.14      
 [58] ggridges_0.5.4         survival_3.5-5         foreach_1.5.2         
 [61] tools_4.3.0            ica_1.0-3              Rcpp_1.0.10           
 [64] glue_1.6.2             gridExtra_2.3          xfun_0.39             
 [67] withr_2.5.0            fastmap_1.1.1          fansi_1.0.4           
 [70] callr_3.7.3            digest_0.6.31          timechange_0.2.0      
 [73] R6_2.5.1               mime_0.12              colorspace_2.1-0      
 [76] scattermore_1.2        tensor_1.5             spatstat.data_3.0-1   
 [79] utf8_1.2.3             generics_0.1.3         data.table_1.14.8     
 [82] httr_1.4.6             htmlwidgets_1.6.2      whisker_0.4.1         
 [85] uwot_0.1.14            pkgconfig_2.0.3        gtable_0.3.3          
 [88] lmtest_0.9-40          htmltools_0.5.5        carData_3.0-5         
 [91] dotCall64_1.0-2        clue_0.3-64            scales_1.2.1          
 [94] png_0.1-8              knitr_1.43             rstudioapi_0.14       
 [97] rjson_0.2.21           tzdb_0.4.0             reshape2_1.4.4        
[100] nlme_3.1-162           curl_5.0.0             cachem_1.0.8          
[103] zoo_1.8-12             GlobalOptions_0.1.2    KernSmooth_2.23-21    
[106] parallel_4.3.0         miniUI_0.1.1.1         pillar_1.9.0          
[109] vctrs_0.6.2            RANN_2.6.1             promises_1.2.0.1      
[112] car_3.1-2              xtable_1.8-4           cluster_2.1.4         
[115] evaluate_0.21          cli_3.6.1              compiler_4.3.0        
[118] rlang_1.1.1            crayon_1.5.2           future.apply_1.11.0   
[121] ggsignif_0.6.4         labeling_0.4.2         ps_1.7.5              
[124] getPass_0.2-4          fs_1.6.2               stringi_1.7.12        
[127] deldir_1.0-9           assertthat_0.2.1       munsell_0.5.0         
[130] lazyeval_0.2.2         spatstat.geom_3.2-1    Matrix_1.6-5          
[133] RcppHNSW_0.5.0         hms_1.1.3              bit64_4.0.5           
[136] future_1.32.0          shiny_1.7.4            highr_0.10            
[139] ROCR_1.0-11            gargle_1.4.0           igraph_1.4.3          
[142] broom_1.0.4            bslib_0.4.2            bit_4.0.5